CFU
8
Length
14 Weeks
Semester DD
First
Signal Processing: Sampling of the Signal, Sampling Theorem, Convolution, Correlation (week 1).
Spectral Analysis I: The Fourier Transform, DFT, FFT, Power Spectrum, Phases (week 2-3-4)
The Noise: Noise Sources, Noise Types and Spectra, SNR maximization, Noise. suppression (week 5).
Introduction to Machine Learning on Astrophysical data: PCA, clustering, classification, ARMA (week 6-7).
Spectral Analysis II: wavelets, Adaptive Methods, EMD (week 8-9).
Communicating your research: How to write the project report in the context of writing a scientific paper (week 10).
In the LAB:
Data Access: FITS + multiFITS.
Datafication examples: H-R diagram, Hubble data.
The Fourier Transform: Fourier spectrum, Digital Filters, Data manipulation.
Data-cubes Analysis: Wavelet Spectra, EMD analysis.
LEARNING OUTCOMES:
The aim of the course is to provide to the student a broad overview of the various methods and techniques of data analysis, with a deeper insight on those used in modern-time astrophysics. In particular, we will study the aspects of digital information access, handling, restoration, manipulation, compression and transformation into data.
KNOWLEDGE AND UNDERSTANDING:
Students must achieve a deep understanding of the implication of data digitization procedures and the associated effects. They must also achieve a good comprehension of the state of the art in data analysis techniques, in particular in the analysis of data from Physics and Astrophysics experiments.
APPLYING KNOWLEDGE AND UNDERSTANDING:
Students must be able to identify the key elements of a physical data-sets, with specific knowledge on the aspects of digital information access, handling, restoration, manipulation, compression and transformation into data, with focus on astrophysical data-sets. They must be able to use and implement algorithms for Data Restoring and Analysis, and for Machine Learning. They must be able to comprehend, critically evaluate, and adapt to new experimental data, existing algorithms. Students must developed advanced skills in programming in Python or an analogous programming language.
MAKING JUDGEMENTS:
Students must be able to autonomously run data analysis algorithms and numerical simulations. They must be able to perform bibliographic researches on the topic of Data Analysis and Machine Learning and to autonomously select the relevant results, in particular on the Web. This capabilities are achieved through the study to prepare for the exam, during the laboratory activities and examining in depth specific topics by critically analyzing journal papers.
COMMUNICATION SKILLS:
Students must be able to work in an interdisciplinary group. They must be able to present their own research or the results of a bibliographic research both to the experts and to amateurs.
LEARNING SKILLS:
Students must be able to take on a new research field in Data Analysis or Machine Learning by autonomous study.